Graph-based representations of molecules have also been used for molecular generation tasks. Jin et al. [1]} use graph-structured encoder and junction tree-based decoder to generate molecular structures. While graph-structured generative models allow for greater representation of long-range atomic interactions and provide geometric information of the molecule, graph-structured generative models have been restricted to a small and limited set of molecules [2]}. On the other hand. MPNN, CGCNN, SchNet, and GemNet, achieve state-of-the-art accuracy in molecular property prediction, and molecular dynamics are achieved by graph-based neural networks [3]}, [4]}, [5]}, [6]}.
Yet, these models are not suitable for use in unsupervised training and generative modeling. Graph-based generators are required to sample discrete structures, i.e graphs, from a continuous latent space. This results in poor performance when the discrete structure is too large [2]}.
